News surprises and volatility spillover among agricultural commodities: The case of corn, wheat, soybean and soybean oil

Hamadi, Hassan; Bassil, Charbel; Nehme, Tamara

doi:10.1016/j.ribaf.2017.04.006

Cited by 36 publications

(23 citation statements)

References 30 publications

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“…First, during the last several years, the volatility of agricultural commodity prices seems to have increased. As argued by Hamadi et al (2017), during the period of 2007-2009, agricultural commodity prices exhibited large and unexpected variations. In support of this thesis, the Food Price Index of the Food and Agriculture Organization (FAO) was quite unchanged before 2007, after which it drastically grew until March 2008.…”

Section: Introductionmentioning

confidence: 96%

“…Among these, Nazlioglu et al (2013), using daily data spanning from January 1986 to March 2011 concerning the spot prices of the world's oil, corn, soybeans, wheat, and sugar, found that, after the Great Recession period, the oil market transferred volatility to three agricultural commodities (corn, wheat, and soybean). Recently, Hamadi et al (2017) found significant bidirectional volatility spillovers among four agricultural commodities (corn, wheat, soybeans, and soybean oil) by employing daily data covering the period from December 1999 to May 2015. In addition, Śmiech et al (2018) investigated the volatility spillovers among four agricultural commodities (corn, soybean, wheat and rice) and some financial and energy variables (US dollar, S&P500 futures and crude oil).…”

Section: Introductionmentioning

confidence: 99%

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On the Volatility Spillover between Agricultural Commodities and Latin American Stock Markets

Candila

Farace

2018

Risks

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Addressing the volatility spillovers of agricultural commodities is important for at least two reasons. First, for the last several years, the volatility of agricultural commodity prices seems to have increased. Second, according to the Food and Agriculture Organization, there is a strong need for understanding the potential (negative) impacts on food security caused by food commodity volatilities. This paper aims at investigating the presence, the size, and the persistence of volatility spillovers among five agricultural commodities (corn, sugar, wheat, soybean, and bioethanol) and five Latin American (Argentina, Brazil, Chile, Colombia, Peru) stock market indexes. Overall, when a negative shock hits the commodity market, Latin American stock market volatility tends to increase. This happens, for instance, for the relationships from corn to Chile and Colombia and from wheat to Peru and Chile.

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Section: Introductionmentioning

confidence: 96%

Section: Introductionmentioning

confidence: 99%

On the Volatility Spillover between Agricultural Commodities and Latin American Stock Markets

Candila

Farace

2018

Risks

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“…During the last decade, commodities became included in portfolio diversification. Commodity financialization (increase in investments in commodities through financial instruments), which took effect between 2004 and 2005, has generated an increase in integration within commodity markets and, in particular, in the agricultural commodity sector [ 56 ]. Differently than other types of commodities, agricultural commodities exhibited unexpected extreme fluctuations, especially during the period 2007–2009, which makes market participants such as producers, consumers, and investors to be seriously concerned about the movements of agricultural commodities as well as their co-movements on both single market level and among different markets.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

Permutation Entropy and Statistical Complexity Analysis of Brazilian Agricultural Commodities

Araujo

Bejan

Rosso

et al. 2019

Entropy

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Agricultural commodities are considered perhaps the most important commodities, as any abrupt increase in food prices has serious consequences on food security and welfare, especially in developing countries. In this work, we analyze predictability of Brazilian agricultural commodity prices during the period after 2007/2008 food crisis. We use information theory based method Complexity/Entropy causality plane (CECP) that was shown to be successful in the analysis of market efficiency and predictability. By estimating information quantifiers permutation entropy and statistical complexity, we associate to each commodity the position in CECP and compare their efficiency (lack of predictability) using the deviation from a random process. Coffee market shows highest efficiency (lowest predictability) while pork market shows lowest efficiency (highest predictability). By analyzing temporal evolution of commodities in the complexity–entropy causality plane, we observe that during the analyzed period (after 2007/2008 crisis) the efficiency of cotton, rice, and cattle markets increases, the soybeans market shows the decrease in efficiency until 2012, followed by the lower predictability and the increase of efficiency, while most commodities (8 out of total 12) exhibit relatively stable efficiency, indicating increased market integration in post-crisis period.

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“…Similar to any other product or good, laws and supply and demand also have a VS effect on the prices of oil, and bank prices and production costs have a spillover effect on oil prices. Furthermore, Hamadi, Bassil, and Nehme (2017) highlight that political unrest, natural disasters and other disasters could potentially disrupt manufacturing, which would have a substantial impact and spillover effect and increase the risk of volatility in oil prices.…”

Section: Literature Reviewmentioning

confidence: 99%

Volatility Transmission between Oil Prices and Stock Prices as a New Source of Instability: Lessons from the UK Experience

Robertson

2020

Asian Journal of Economics and Empirical Research

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The banking industry is one of the main regulators of the economy; therefore, a possible decline in performance or risk to operations could trigger a chain of unexpected economic events. In consideration of this, this paper sought to evaluate the risk imposed by the oil and gas sector on the banking industry in the United Kingdom (UK) by evaluating the spillover effects and the exposure of the banking industry to shocks caused by changes in oil prices. In order to reach this objective, the present study evaluated the impact and effect of the volatility of bank stock prices and oil prices in four leading banks in the UK. These banks-HSBC, Royal Bank of Scotland, Lloyds Banking Group and Barclays PLC-were selected on the basis of their involvement in the oil and gas sector, and they were chosen to represent the volatility of the banking industry. The change in price of Brent crude oil was used as a representation of the volatility imposed by the oil industry. The vector autoregressive fractionally integrated moving average (VARFIMA) model was used to evaluate the impact of the volatility spillover and to evaluate the presence of covolatility between certain parameters. The results showed volatility responses between the BSP and oil prices. The Granger causality analysis confirmed the presence of bidirectional causality between the volatility caused by oil prices and the stock prices of banks.

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News surprises and volatility spillover among agricultural commodities: The case of corn, wheat, soybean and soybean oil

Cited by 36 publications

References 30 publications

On the Volatility Spillover between Agricultural Commodities and Latin American Stock Markets

On the Volatility Spillover between Agricultural Commodities and Latin American Stock Markets

Permutation Entropy and Statistical Complexity Analysis of Brazilian Agricultural Commodities

Volatility Transmission between Oil Prices and Stock Prices as a New Source of Instability: Lessons from the UK Experience

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